LLMs & Assistants
DeepSeek vs Qwen.
Both sit in LLMs & Assistants, scored on the same six pillars from the same published methodology. Here is where they actually differ.
The short answer
Too close to call on score alone — DeepSeek sits at 83.6 and Qwen at 84.1. A gap that size is inside the noise of any honest scoring model, so pick on fit rather than rank.
Qwen leads on integrations & ecosystem and maturity & reliability.
DeepSeek AI
83.6
Excellent
Open-weight frontier models at a fraction of the usual cost.
- From
- Free (open weights) / low-cost API
- Pricing
- Open Source
- Maturity
- Emerging
- Founded
- 2023
Alibaba Cloud
84.1
Excellent
Alibaba's open-weight LLM family, strong on code, math, and agentic tasks
- From
- Free (open-weight, self-hosted) / API from ~$0.05 per 1M input tokens
- Pricing
- Freemium
- Maturity
- Established
- Founded
- 2023
Pillar by pillar
The same six pillars and fixed weights used for every tool on the site. A lead of fewer than 5 points is not called for either side — these are evidence-backed judgements, not measurements. Read the methodology.
Capability
Level
Value for Money
Level
Security & Compliance
Level
Integrations & Ecosystem
Qwen by 7
Maturity & Reliability
Qwen by 6
Momentum
Level
Which one, and when
Pick DeepSeek if
- →Cost-sensitive AI workloads
- →Self-hosted frontier reasoning
- →Teams fine-tuning open models
The catch
- Hosted service raises data-residency questions for regulated buyers
- Enterprise support and certifications lag Western labs
Pick Qwen if
- →it has to fit the stack you already run — it leads Integrations & Ecosystem by 7 points.
- →it has to hold up in production from day one — it leads Maturity & Reliability by 6 points.
- →Developers wanting low-cost, self-hostable open-weight LLMs for coding and agentic workflows
- →Enterprises needing multilingual, multimodal models via a single unified API
- →Cost-sensitive production deployments seeking frontier-adjacent performance at a fraction of Western model pricing
The catch
- Newer flagship tiers (Qwen3.6/3.7-Max) have shifted to closed weights, limiting self-hosting for the most capable models
- Some large models use the more restrictive Tongyi Qianwen License rather than Apache 2.0, with commercial caps tied to MAU thresholds
What each is good at
DeepSeek
- ✓Frontier-class reasoning and coding at a small fraction of the cost
- ✓Open weights allow self-hosting, fine-tuning and full data control
- ✓API pricing undercuts most closed competitors substantially
Qwen
- ✓Aggressive price-to-performance with open-weight Apache 2.0 models that can be self-hosted at zero per-token cost
- ✓Strong, frequently-updated coding and agentic performance, including high SWE-bench Verified scores
- ✓Broad model catalogue spanning text, vision, audio, coding and embeddings under one API
Other comparisons in LLMs & Assistants
Comparing something else? Build your own side-by-side across any tools in the catalog.